Overview

Dataset statistics

Number of variables23
Number of observations140051
Missing cells136020
Missing cells (%)4.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.6 MiB
Average record size in memory184.0 B

Variable types

NUM19
CAT4

Warnings

AREA_TITLE has a high cardinality: 396 distinct values High cardinality
PRIM_STATE has a high cardinality: 52 distinct values High cardinality
OCC_TITLE has a high cardinality: 808 distinct values High cardinality
A_MEAN is highly correlated with H_MEAN and 8 other fieldsHigh correlation
H_MEAN is highly correlated with A_MEAN and 8 other fieldsHigh correlation
H_PCT25 is highly correlated with H_MEAN and 8 other fieldsHigh correlation
H_PCT10 is highly correlated with H_PCT25 and 4 other fieldsHigh correlation
H_MEDIAN is highly correlated with H_MEAN and 10 other fieldsHigh correlation
H_PCT75 is highly correlated with H_MEAN and 8 other fieldsHigh correlation
H_PCT90 is highly correlated with H_MEAN and 6 other fieldsHigh correlation
A_PCT10 is highly correlated with H_PCT10 and 4 other fieldsHigh correlation
A_PCT25 is highly correlated with H_MEAN and 8 other fieldsHigh correlation
A_MEDIAN is highly correlated with H_MEAN and 10 other fieldsHigh correlation
A_PCT75 is highly correlated with H_MEAN and 8 other fieldsHigh correlation
A_PCT90 is highly correlated with H_MEAN and 6 other fieldsHigh correlation
TOT_EMP has 11658 (8.3%) missing values Missing
EMP_PRSE has 11658 (8.3%) missing values Missing
JOBS_1000 has 11658 (8.3%) missing values Missing
LOC_QUOTIENT has 11658 (8.3%) missing values Missing
H_MEAN has 9284 (6.6%) missing values Missing
A_MEAN has 2848 (2.0%) missing values Missing
MEAN_PRSE has 2976 (2.1%) missing values Missing
H_PCT10 has 9305 (6.6%) missing values Missing
H_PCT25 has 9543 (6.8%) missing values Missing
H_MEDIAN has 10152 (7.2%) missing values Missing
H_PCT75 has 10932 (7.8%) missing values Missing
H_PCT90 has 13146 (9.4%) missing values Missing
A_PCT10 has 2869 (2.0%) missing values Missing
A_PCT25 has 3108 (2.2%) missing values Missing
A_MEDIAN has 3723 (2.7%) missing values Missing
A_PCT75 has 4546 (3.2%) missing values Missing
A_PCT90 has 6956 (5.0%) missing values Missing
TOT_EMP is highly skewed (γ1 = 99.04921957) Skewed
LOC_QUOTIENT is highly skewed (γ1 = 39.84715828) Skewed
EMP_PRSE has 2729 (1.9%) zeros Zeros

Reproduction

Analysis started2021-10-31 19:41:49.669632
Analysis finished2021-10-31 19:44:10.667085
Duration2 minutes and 21 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

AREA
Real number (ℝ≥0)

Distinct396
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32514.37376
Minimum10180
Maximum79600
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:10.777028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10180
5-th percentile12100
Q120220
median31080
Q341620
95-th percentile71650
Maximum79600
Range69420
Interquartile range (IQR)21400

Descriptive statistics

Standard deviation15208.39888
Coefficient of variation (CV)0.467743866
Kurtosis1.142376048
Mean32514.37376
Median Absolute Deviation (MAD)10620
Skewness0.9576739483
Sum4553670560
Variance231295396.6
MonotocityIncreasing
2021-10-31T13:44:11.014074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
356207560.5%
 
169807410.5%
 
310807410.5%
 
379807160.5%
 
716507150.5%
 
479007130.5%
 
191007100.5%
 
264207040.5%
 
426606980.5%
 
331006970.5%
 
Other values (386)13286094.9%
 
ValueCountFrequency (%) 
101802810.2%
 
103801760.1%
 
104204960.4%
 
105002330.2%
 
105402390.2%
 
ValueCountFrequency (%) 
796004850.3%
 
787002690.2%
 
781005030.4%
 
772005980.4%
 
769003330.2%
 

AREA_TITLE
Categorical

HIGH CARDINALITY

Distinct396
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
New York-Newark-Jersey City, NY-NJ-PA
 
756
Los Angeles-Long Beach-Anaheim, CA
 
741
Chicago-Naperville-Elgin, IL-IN-WI
 
741
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
 
716
Boston-Cambridge-Nashua, MA-NH
 
715
Other values (391)
136382 
ValueCountFrequency (%) 
New York-Newark-Jersey City, NY-NJ-PA7560.5%
 
Los Angeles-Long Beach-Anaheim, CA7410.5%
 
Chicago-Naperville-Elgin, IL-IN-WI7410.5%
 
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD7160.5%
 
Boston-Cambridge-Nashua, MA-NH7150.5%
 
Washington-Arlington-Alexandria, DC-VA-MD-WV7130.5%
 
Dallas-Fort Worth-Arlington, TX7100.5%
 
Houston-The Woodlands-Sugar Land, TX7040.5%
 
Seattle-Tacoma-Bellevue, WA6980.5%
 
Miami-Fort Lauderdale-West Palm Beach, FL6970.5%
 
Other values (386)13286094.9%
 
2021-10-31T13:44:11.332628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-31T13:44:11.611661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length46
Median length17
Mean length20.13839958
Min length8

PRIM_STATE
Categorical

HIGH CARDINALITY

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
CA
10713 
TX
 
9320
FL
 
8318
PA
 
6406
NC
 
5420
Other values (47)
99874 
ValueCountFrequency (%) 
CA107137.6%
 
TX93206.7%
 
FL83185.9%
 
PA64064.6%
 
NC54203.9%
 
OH48453.5%
 
NY47503.4%
 
MI44323.2%
 
GA41823.0%
 
WI40662.9%
 
Other values (42)7759955.4%
 
2021-10-31T13:44:11.891615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-31T13:44:12.239912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

OCC_CODE
Real number (ℝ≥0)

Distinct808
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean337441.9448
Minimum0
Maximum537199
Zeros396
Zeros (%)0.3%
Memory size1.1 MiB
2021-10-31T13:44:12.501829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile119051
Q1212011
median339098
Q3472021
95-th percentile519198
Maximum537199
Range537199
Interquartile range (IQR)260010

Descriptive statistics

Standard deviation136456.9079
Coefficient of variation (CV)0.404386325
Kurtosis-1.233256796
Mean337441.9448
Median Absolute Deviation (MAD)128005
Skewness-0.1863556408
Sum4.725908181e+10
Variance1.862048773e+10
MonotocityNot monotonic
2021-10-31T13:44:12.759831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03960.3%
 
4120113960.3%
 
5330323960.3%
 
4120313960.3%
 
4390613960.3%
 
5100003960.3%
 
4110113960.3%
 
3900003960.3%
 
5300003960.3%
 
3530233960.3%
 
Other values (798)13609197.2%
 
ValueCountFrequency (%) 
03960.3%
 
1100003960.3%
 
1110113180.2%
 
1110213960.3%
 
1110311890.1%
 
ValueCountFrequency (%) 
53719967< 0.1%
 
53712127< 0.1%
 
5370812650.2%
 
53707322< 0.1%
 
53707230< 0.1%
 

OCC_TITLE
Categorical

HIGH CARDINALITY

Distinct808
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Office and Administrative Support Occupations
 
396
Construction and Extraction Occupations
 
396
Maintenance and Repair Workers, General
 
396
All Occupations
 
396
First-Line Supervisors of Retail Sales Workers
 
396
Other values (803)
138071 
ValueCountFrequency (%) 
Office and Administrative Support Occupations3960.3%
 
Construction and Extraction Occupations3960.3%
 
Maintenance and Repair Workers, General3960.3%
 
All Occupations3960.3%
 
First-Line Supervisors of Retail Sales Workers3960.3%
 
General and Operations Managers3960.3%
 
Heavy and Tractor-Trailer Truck Drivers3960.3%
 
Office Clerks, General3960.3%
 
Building and Grounds Cleaning and Maintenance Occupations3960.3%
 
Food Preparation and Serving Related Occupations3960.3%
 
Other values (798)13609197.2%
 
2021-10-31T13:44:13.070080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6 ?
Unique (%)< 0.1%
2021-10-31T13:44:13.392030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length136
Median length35
Mean length37.64073088
Min length6

O_GROUP
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
detailed
131010 
major
 
8645
total
 
396
ValueCountFrequency (%) 
detailed13101093.5%
 
major86456.2%
 
total3960.3%
 
2021-10-31T13:44:13.651010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-31T13:44:13.804015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:13.986972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length7.806334835
Min length5

TOT_EMP
Real number (ℝ≥0)

MISSING
SKEWED

Distinct3807
Distinct (%)3.0%
Missing11658
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean2832.213672
Minimum30
Maximum8834370
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:14.238964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile40
Q190
median230
Q3780
95-th percentile6540
Maximum8834370
Range8834340
Interquartile range (IQR)690

Descriptive statistics

Standard deviation45868.87764
Coefficient of variation (CV)16.19541565
Kurtosis14489.76716
Mean2832.213672
Median Absolute Deviation (MAD)180
Skewness99.04921957
Sum363636410
Variance2103953936
MonotocityNot monotonic
2021-10-31T13:44:14.473013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4068734.9%
 
5060784.3%
 
6052623.8%
 
7046703.3%
 
8043593.1%
 
3040072.9%
 
9038122.7%
 
10034092.4%
 
11030692.2%
 
12028132.0%
 
Other values (3797)8404160.0%
 
(Missing)116588.3%
 
ValueCountFrequency (%) 
3040072.9%
 
4068734.9%
 
5060784.3%
 
6052623.8%
 
7046703.3%
 
ValueCountFrequency (%) 
88343701< 0.1%
 
58225101< 0.1%
 
43621101< 0.1%
 
35884501< 0.1%
 
30222001< 0.1%
 

EMP_PRSE
Real number (ℝ≥0)

MISSING
ZEROS

Distinct500
Distinct (%)0.4%
Missing11658
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean18.46889939
Minimum0
Maximum49.9
Zeros2729
Zeros (%)1.9%
Memory size1.1 MiB
2021-10-31T13:44:14.716018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.8
Q19.1
median16
Q326.1
95-th percentile42.2
Maximum49.9
Range49.9
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.97463584
Coefficient of variation (CV)0.6483675928
Kurtosis-0.3547705279
Mean18.46889939
Median Absolute Deviation (MAD)8
Skewness0.6643197638
Sum2371277.4
Variance143.3919036
MonotocityNot monotonic
2021-10-31T13:44:14.957014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
027291.9%
 
8.95370.4%
 
8.45290.4%
 
9.65270.4%
 
10.45270.4%
 
10.95260.4%
 
7.95230.4%
 
11.45180.4%
 
7.45130.4%
 
8.75130.4%
 
Other values (490)12095186.4%
 
(Missing)116588.3%
 
ValueCountFrequency (%) 
027291.9%
 
0.153< 0.1%
 
0.2720.1%
 
0.3770.1%
 
0.4750.1%
 
ValueCountFrequency (%) 
49.964< 0.1%
 
49.8830.1%
 
49.7740.1%
 
49.668< 0.1%
 
49.567< 0.1%
 

JOBS_1000
Real number (ℝ≥0)

MISSING

Distinct21772
Distinct (%)17.0%
Missing11658
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean8.856372217
Minimum0.004
Maximum1000
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:15.214016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.131
Q10.554
median1.35
Q33.689
95-th percentile28.5044
Maximum1000
Range999.996
Interquartile range (IQR)3.135

Descriptive statistics

Standard deviation57.1884465
Coefficient of variation (CV)6.457321926
Kurtosis275.4692133
Mean8.856372217
Median Absolute Deviation (MAD)1.017
Skewness16.14843491
Sum1137096.198
Variance3270.518413
MonotocityNot monotonic
2021-10-31T13:44:15.456962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10003960.3%
 
0.224850.1%
 
0.274840.1%
 
0.28830.1%
 
0.555830.1%
 
0.419830.1%
 
0.199830.1%
 
0.149820.1%
 
0.115820.1%
 
0.271810.1%
 
Other values (21762)12725190.9%
 
(Missing)116588.3%
 
ValueCountFrequency (%) 
0.0042< 0.1%
 
0.0053< 0.1%
 
0.0061< 0.1%
 
0.0075< 0.1%
 
0.0086< 0.1%
 
ValueCountFrequency (%) 
10003960.3%
 
359.0021< 0.1%
 
242.4051< 0.1%
 
238.5051< 0.1%
 
233.211< 0.1%
 

LOC_QUOTIENT
Real number (ℝ≥0)

MISSING
SKEWED

Distinct1511
Distinct (%)1.2%
Missing11658
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean1.301940526
Minimum0.03
Maximum319.37
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:15.705082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.34
Q10.68
median0.97
Q31.38
95-th percentile2.91
Maximum319.37
Range319.34
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation2.446354709
Coefficient of variation (CV)1.879006498
Kurtosis3338.29665
Mean1.301940526
Median Absolute Deviation (MAD)0.33
Skewness39.84715828
Sum167160.05
Variance5.98465136
MonotocityNot monotonic
2021-10-31T13:44:15.936019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
114591.0%
 
0.811780.8%
 
0.8311410.8%
 
0.8211410.8%
 
0.9111370.8%
 
0.8411250.8%
 
0.8711180.8%
 
0.9411150.8%
 
0.7911120.8%
 
0.8611110.8%
 
Other values (1501)11675683.4%
 
(Missing)116588.3%
 
ValueCountFrequency (%) 
0.031< 0.1%
 
0.049< 0.1%
 
0.058< 0.1%
 
0.0622< 0.1%
 
0.0720< 0.1%
 
ValueCountFrequency (%) 
319.371< 0.1%
 
218.171< 0.1%
 
166.241< 0.1%
 
129.651< 0.1%
 
115.511< 0.1%
 

H_MEAN
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7980
Distinct (%)6.1%
Missing9284
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean27.17055129
Minimum8.32
Maximum151.84
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:16.274042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8.32
5-th percentile12.02
Q117.08
median22.94
Q332.59
95-th percentile55.27
Maximum151.84
Range143.52
Interquartile range (IQR)15.51

Descriptive statistics

Standard deviation15.45700235
Coefficient of variation (CV)0.568888065
Kurtosis9.941217719
Mean27.17055129
Median Absolute Deviation (MAD)6.97
Skewness2.479876637
Sum3553011.48
Variance238.9189215
MonotocityNot monotonic
2021-10-31T13:44:16.577036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
16.4900.1%
 
17.68860.1%
 
16.56860.1%
 
17.61830.1%
 
17.01810.1%
 
18.15800.1%
 
16.86790.1%
 
17.62790.1%
 
17.29790.1%
 
18.83770.1%
 
Other values (7970)12994792.8%
 
(Missing)92846.6%
 
ValueCountFrequency (%) 
8.321< 0.1%
 
8.332< 0.1%
 
8.352< 0.1%
 
8.382< 0.1%
 
8.392< 0.1%
 
ValueCountFrequency (%) 
151.841< 0.1%
 
1511< 0.1%
 
149.641< 0.1%
 
148.941< 0.1%
 
148.11< 0.1%
 

A_MEAN
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct14142
Distinct (%)10.3%
Missing2848
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean57081.51556
Minimum17300
Maximum315830
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:16.880038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum17300
5-th percentile25100
Q135860
median48560
Q368900
95-th percentile115119
Maximum315830
Range298530
Interquartile range (IQR)33040

Descriptive statistics

Standard deviation32034.06981
Coefficient of variation (CV)0.5611986559
Kurtosis9.566794437
Mean57081.51556
Median Absolute Deviation (MAD)14950
Skewness2.408721786
Sum7831755180
Variance1026181629
MonotocityNot monotonic
2021-10-31T13:44:17.138448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3789047< 0.1%
 
3969047< 0.1%
 
3428046< 0.1%
 
3677046< 0.1%
 
3745045< 0.1%
 
4210045< 0.1%
 
3411045< 0.1%
 
3664045< 0.1%
 
3368044< 0.1%
 
3507044< 0.1%
 
Other values (14132)13674997.6%
 
(Missing)28482.0%
 
ValueCountFrequency (%) 
173001< 0.1%
 
173302< 0.1%
 
173601< 0.1%
 
173701< 0.1%
 
174301< 0.1%
 
ValueCountFrequency (%) 
3158301< 0.1%
 
3140801< 0.1%
 
3112601< 0.1%
 
3098001< 0.1%
 
3080501< 0.1%
 

MEAN_PRSE
Real number (ℝ≥0)

MISSING

Distinct296
Distinct (%)0.2%
Missing2976
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean5.296913369
Minimum0.4
Maximum29.9
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:17.398445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1.8
Q12.9
median4.2
Q36.4
95-th percentile12.6
Maximum29.9
Range29.5
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation3.659072129
Coefficient of variation (CV)0.6907932743
Kurtosis6.98808419
Mean5.296913369
Median Absolute Deviation (MAD)1.5
Skewness2.243364742
Sum726074.4
Variance13.38880885
MonotocityNot monotonic
2021-10-31T13:44:17.628487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.929972.1%
 
2.729692.1%
 
329382.1%
 
3.329372.1%
 
3.129112.1%
 
2.629082.1%
 
3.229012.1%
 
3.528962.1%
 
2.828792.1%
 
3.428722.1%
 
Other values (286)10786777.0%
 
(Missing)29762.1%
 
ValueCountFrequency (%) 
0.42< 0.1%
 
0.510< 0.1%
 
0.633< 0.1%
 
0.741< 0.1%
 
0.870< 0.1%
 
ValueCountFrequency (%) 
29.93< 0.1%
 
29.87< 0.1%
 
29.77< 0.1%
 
29.62< 0.1%
 
29.55< 0.1%
 

H_PCT10
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct4985
Distinct (%)3.8%
Missing9305
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean16.91676013
Minimum7.25
Maximum99.86
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:17.888494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7.25
5-th percentile8.51
Q111.58
median14.43
Q319.79
95-th percentile32.9
Maximum99.86
Range92.61
Interquartile range (IQR)8.21

Descriptive statistics

Standard deviation8.584591168
Coefficient of variation (CV)0.5074607136
Kurtosis12.74854636
Mean16.91676013
Median Absolute Deviation (MAD)3.69
Skewness2.706131864
Sum2211798.72
Variance73.69520553
MonotocityNot monotonic
2021-10-31T13:44:18.197824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13.029880.7%
 
13.019470.7%
 
13.523090.2%
 
12.772680.2%
 
18.152600.2%
 
13.512550.2%
 
102540.2%
 
10.012380.2%
 
18.562270.2%
 
12.762100.1%
 
Other values (4975)12679090.5%
 
(Missing)93056.6%
 
ValueCountFrequency (%) 
7.251< 0.1%
 
7.261< 0.1%
 
7.321< 0.1%
 
7.331< 0.1%
 
7.381< 0.1%
 
ValueCountFrequency (%) 
99.861< 0.1%
 
99.791< 0.1%
 
99.61< 0.1%
 
99.591< 0.1%
 
99.371< 0.1%
 

H_PCT25
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct5967
Distinct (%)4.6%
Missing9543
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean20.33493548
Minimum7.26
Maximum99.92
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:18.513842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7.26
5-th percentile9.51
Q113.34
median17.32
Q324.23
95-th percentile40.59
Maximum99.92
Range92.66
Interquartile range (IQR)10.89

Descriptive statistics

Standard deviation10.63222575
Coefficient of variation (CV)0.5228551504
Kurtosis8.1721768
Mean20.33493548
Median Absolute Deviation (MAD)4.78
Skewness2.244842299
Sum2653871.76
Variance113.0442244
MonotocityNot monotonic
2021-10-31T13:44:18.814295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13.026720.5%
 
18.573460.2%
 
18.162620.2%
 
13.521920.1%
 
13.011700.1%
 
10.011390.1%
 
13.511270.1%
 
12.011200.1%
 
11.011200.1%
 
15.011200.1%
 
Other values (5957)12824091.6%
 
(Missing)95436.8%
 
ValueCountFrequency (%) 
7.261< 0.1%
 
7.821< 0.1%
 
7.833< 0.1%
 
7.841< 0.1%
 
7.852< 0.1%
 
ValueCountFrequency (%) 
99.921< 0.1%
 
99.91< 0.1%
 
99.891< 0.1%
 
99.881< 0.1%
 
99.841< 0.1%
 

H_MEDIAN
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct6812
Distinct (%)5.2%
Missing10152
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean25.05029746
Minimum8.01
Maximum99.99
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:19.248449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8.01
5-th percentile11.169
Q115.99
median21.69
Q330.46
95-th percentile50.36
Maximum99.99
Range91.98
Interquartile range (IQR)14.47

Descriptive statistics

Standard deviation12.78164494
Coefficient of variation (CV)0.5102392479
Kurtosis3.646514873
Mean25.05029746
Median Absolute Deviation (MAD)6.72
Skewness1.62257236
Sum3254008.59
Variance163.3704473
MonotocityNot monotonic
2021-10-31T13:44:19.546451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
13.021510.1%
 
14.37950.1%
 
19.21950.1%
 
17.91930.1%
 
17.42920.1%
 
17.66910.1%
 
18.97900.1%
 
18.51880.1%
 
14.74870.1%
 
18.23870.1%
 
Other values (6802)12893092.1%
 
(Missing)101527.2%
 
ValueCountFrequency (%) 
8.011< 0.1%
 
8.333< 0.1%
 
8.351< 0.1%
 
8.361< 0.1%
 
8.382< 0.1%
 
ValueCountFrequency (%) 
99.991< 0.1%
 
99.971< 0.1%
 
99.791< 0.1%
 
99.781< 0.1%
 
99.751< 0.1%
 

H_PCT75
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7955
Distinct (%)6.2%
Missing10932
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean31.01998033
Minimum8.84
Maximum99.96
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:19.857463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8.84
5-th percentile13.17
Q119.37
median27.13
Q338.48
95-th percentile63.01
Maximum99.96
Range91.12
Interquartile range (IQR)19.11

Descriptive statistics

Standard deviation15.58891126
Coefficient of variation (CV)0.502544202
Kurtosis1.570378605
Mean31.01998033
Median Absolute Deviation (MAD)8.74
Skewness1.26801699
Sum4005268.84
Variance243.0141543
MonotocityNot monotonic
2021-10-31T13:44:20.153450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
31.272980.2%
 
30.292500.2%
 
30.282170.2%
 
31.261310.1%
 
18.981010.1%
 
19.05870.1%
 
18.47840.1%
 
19.1790.1%
 
18.97780.1%
 
18.82770.1%
 
Other values (7945)12771791.2%
 
(Missing)109327.8%
 
ValueCountFrequency (%) 
8.843< 0.1%
 
8.862< 0.1%
 
8.891< 0.1%
 
8.91< 0.1%
 
8.911< 0.1%
 
ValueCountFrequency (%) 
99.961< 0.1%
 
99.951< 0.1%
 
99.932< 0.1%
 
99.861< 0.1%
 
99.841< 0.1%
 

H_PCT90
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct8738
Distinct (%)6.9%
Missing13146
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean37.07292053
Minimum9.14
Maximum99.99
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:20.504512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9.14
5-th percentile15.36
Q123.82
median31.77
Q347.12
95-th percentile75.87
Maximum99.99
Range90.85
Interquartile range (IQR)23.3

Descriptive statistics

Standard deviation18.21480777
Coefficient of variation (CV)0.4913237887
Kurtosis0.7261387311
Mean37.07292053
Median Absolute Deviation (MAD)10.01
Skewness1.080225904
Sum4704738.98
Variance331.7792222
MonotocityNot monotonic
2021-10-31T13:44:20.803461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
30.292610.2%
 
30.891890.1%
 
30.91590.1%
 
31.271140.1%
 
24.96920.1%
 
31.93830.1%
 
31.49770.1%
 
24.15740.1%
 
24.33740.1%
 
19.51740.1%
 
Other values (8728)12570889.8%
 
(Missing)131469.4%
 
ValueCountFrequency (%) 
9.143< 0.1%
 
9.161< 0.1%
 
9.171< 0.1%
 
9.212< 0.1%
 
9.221< 0.1%
 
ValueCountFrequency (%) 
99.991< 0.1%
 
99.981< 0.1%
 
99.971< 0.1%
 
99.963< 0.1%
 
99.931< 0.1%
 

A_PCT10
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct8808
Distinct (%)6.4%
Missing2869
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean35433.15041
Minimum15090
Maximum207720
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:21.072510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum15090
5-th percentile17690
Q124210
median30470
Q341810
95-th percentile68370
Maximum207720
Range192630
Interquartile range (IQR)17600

Descriptive statistics

Standard deviation17779.05596
Coefficient of variation (CV)0.5017633417
Kurtosis12.28425129
Mean35433.15041
Median Absolute Deviation (MAD)8000
Skewness2.624782092
Sum4860790440
Variance316094830.8
MonotocityNot monotonic
2021-10-31T13:44:21.320494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2707011840.8%
 
270805630.4%
 
377602820.2%
 
281102520.2%
 
208102350.2%
 
386102170.2%
 
265501970.1%
 
281201650.1%
 
249701510.1%
 
228901420.1%
 
Other values (8798)13379495.5%
 
(Missing)28692.0%
 
ValueCountFrequency (%) 
150902< 0.1%
 
152402< 0.1%
 
153501< 0.1%
 
155702< 0.1%
 
155901< 0.1%
 
ValueCountFrequency (%) 
2077201< 0.1%
 
2075701< 0.1%
 
2071801< 0.1%
 
2071501< 0.1%
 
2067001< 0.1%
 

A_PCT25
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct10634
Distinct (%)7.8%
Missing3108
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean42665.57064
Minimum15100
Maximum207840
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:21.590494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum15100
5-th percentile19770
Q127890
median36540
Q351450
95-th percentile84410
Maximum207840
Range192740
Interquartile range (IQR)23560

Descriptive statistics

Standard deviation22052.00814
Coefficient of variation (CV)0.5168572179
Kurtosis7.825277821
Mean42665.57064
Median Absolute Deviation (MAD)10330
Skewness2.17129169
Sum5842751240
Variance486291063.1
MonotocityNot monotonic
2021-10-31T13:44:21.955531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
270804170.3%
 
270703250.2%
 
386203100.2%
 
377702020.1%
 
281201110.1%
 
208101050.1%
 
28110990.1%
 
20820820.1%
 
37760780.1%
 
2497070< 0.1%
 
Other values (10624)13514496.5%
 
(Missing)31082.2%
 
ValueCountFrequency (%) 
151001< 0.1%
 
162701< 0.1%
 
162802< 0.1%
 
162901< 0.1%
 
163202< 0.1%
 
ValueCountFrequency (%) 
2078401< 0.1%
 
2078001< 0.1%
 
2077601< 0.1%
 
2077401< 0.1%
 
2076801< 0.1%
 

A_MEDIAN
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct12568
Distinct (%)9.2%
Missing3723
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean52678.29844
Minimum16660
Maximum207980
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:22.332619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum16660
5-th percentile23260
Q133620
median45950
Q364320
95-th percentile105046.5
Maximum207980
Range191320
Interquartile range (IQR)30700

Descriptive statistics

Standard deviation26641.40816
Coefficient of variation (CV)0.5057378265
Kurtosis3.419860842
Mean52678.29844
Median Absolute Deviation (MAD)14450
Skewness1.563209933
Sum7181527070
Variance709764628.7
MonotocityNot monotonic
2021-10-31T13:44:22.615710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
270801230.1%
 
3995057< 0.1%
 
3065054< 0.1%
 
4820054< 0.1%
 
4893054< 0.1%
 
5116052< 0.1%
 
5072051< 0.1%
 
3492050< 0.1%
 
2989050< 0.1%
 
3793050< 0.1%
 
Other values (12558)13573396.9%
 
(Missing)37232.7%
 
ValueCountFrequency (%) 
166601< 0.1%
 
173303< 0.1%
 
173701< 0.1%
 
173801< 0.1%
 
174201< 0.1%
 
ValueCountFrequency (%) 
2079801< 0.1%
 
2079301< 0.1%
 
2075501< 0.1%
 
2075401< 0.1%
 
2074801< 0.1%
 

A_PCT75
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct15065
Distinct (%)11.1%
Missing4546
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean65320.53799
Minimum18380
Maximum207930
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:23.032237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum18380
5-th percentile27580
Q140630
median57690
Q381080
95-th percentile131690
Maximum207930
Range189550
Interquartile range (IQR)40450

Descriptive statistics

Standard deviation32615.86012
Coefficient of variation (CV)0.4993201392
Kurtosis1.450675621
Mean65320.53799
Median Absolute Deviation (MAD)18860
Skewness1.226531163
Sum8851259500
Variance1063794331
MonotocityNot monotonic
2021-10-31T13:44:23.305217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
650302760.2%
 
629902210.2%
 
630001730.1%
 
650401080.1%
 
8321053< 0.1%
 
3948052< 0.1%
 
6298047< 0.1%
 
8440046< 0.1%
 
3963045< 0.1%
 
4758045< 0.1%
 
Other values (15055)13443996.0%
 
(Missing)45463.2%
 
ValueCountFrequency (%) 
183803< 0.1%
 
184201< 0.1%
 
184301< 0.1%
 
185001< 0.1%
 
185101< 0.1%
 
ValueCountFrequency (%) 
2079301< 0.1%
 
2079001< 0.1%
 
2078601< 0.1%
 
2078502< 0.1%
 
2077101< 0.1%
 

A_PCT90
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct16939
Distinct (%)12.7%
Missing6956
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean78065.96003
Minimum19010
Maximum207980
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2021-10-31T13:44:23.643185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum19010
5-th percentile32040
Q149980
median67090
Q399510
95-th percentile158710
Maximum207980
Range188970
Interquartile range (IQR)49530

Descriptive statistics

Standard deviation38144.70468
Coefficient of variation (CV)0.4886214768
Kurtosis0.6342443799
Mean78065.96003
Median Absolute Deviation (MAD)21350
Skewness1.044247294
Sum1.039018895e+10
Variance1455018495
MonotocityNot monotonic
2021-10-31T13:44:23.949185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
630002230.2%
 
642601760.1%
 
65040940.1%
 
64270830.1%
 
6425058< 0.1%
 
6641058< 0.1%
 
5191057< 0.1%
 
9974057< 0.1%
 
9975057< 0.1%
 
6299050< 0.1%
 
Other values (16929)13218294.4%
 
(Missing)69565.0%
 
ValueCountFrequency (%) 
190102< 0.1%
 
190201< 0.1%
 
190602< 0.1%
 
191502< 0.1%
 
191801< 0.1%
 
ValueCountFrequency (%) 
2079801< 0.1%
 
2079701< 0.1%
 
2079601< 0.1%
 
2079401< 0.1%
 
2079202< 0.1%
 

Interactions

2021-10-31T13:42:17.453209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:17.751170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:18.028099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:18.314098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:18.584173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:18.890151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:19.229065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:19.533810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:19.841862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:20.125872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:20.413860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:20.716126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:21.017128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:21.286728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:21.560715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:21.851711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:22.166827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:22.464776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:22.763869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:23.051011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:23.323097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:23.589304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:23.850303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:24.112294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:24.390290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:24.725364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:25.101353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:25.494309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:25.759309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:26.018309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:26.283309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:26.555385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:26.806309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:27.059534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:27.326625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:27.607617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:27.876618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:28.158282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:28.441292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:28.705279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:28.950291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:29.195297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:29.435290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:29.681279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:29.944513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:30.222455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:42:30.540455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-10-31T13:43:32.067629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:32.312779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:32.554764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:32.798893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:33.042879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:33.303866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:33.570875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:33.842888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:34.098871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:34.357868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:34.618839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:34.879813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:35.117822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:35.389142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:36.164132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:36.454377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:36.721566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:36.991672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:37.256672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:37.547795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:37.820930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:38.095053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:38.363055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:38.636090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:38.915074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:39.202053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:39.498997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:39.778173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:40.057189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:40.346173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:40.637199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:40.888160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:41.152171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:41.439849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:41.744831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:42.026830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:42.313038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:42.595031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:42.897080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:43.184039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:43.470122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:43.766139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:44.050127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:44.342121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:44.640123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:44.949271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:45.243378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:45.545565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:45.854565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:46.161509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:46.431561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:46.709206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:47.003870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:47.319205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:47.613192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:47.919198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:48.222316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:48.510319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:48.783384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:49.052444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:49.309455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:49.570437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:49.851444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:50.140439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:50.435443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:50.713428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:50.987427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:51.269540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:51.554536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:51.813782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:52.076704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:52.468792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:52.793741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:53.107730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:53.420791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:53.710890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:54.000958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:54.271897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:54.543976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:54.813950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:55.084950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:55.371954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:55.659105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:55.959222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:56.264243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:56.555360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:56.850361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:57.183356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:57.460557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:57.763649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:58.057691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:58.370450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:58.664459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:59.001514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:59.342008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:59.644115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:43:59.910935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:00.202091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:00.499096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:00.792768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:01.115826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:01.443744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:01.774816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:02.060372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:02.345856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:02.625924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:02.923416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:03.188405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:03.463406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:03.752644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:04.060649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:04.346639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:04.646837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-10-31T13:44:24.247216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-31T13:44:24.991200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-31T13:44:25.654166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-31T13:44:26.208954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-10-31T13:44:26.679994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-10-31T13:44:05.504701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:06.942911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:08.264467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-31T13:44:10.078419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

AREAAREA_TITLEPRIM_STATEOCC_CODEOCC_TITLEO_GROUPTOT_EMPEMP_PRSEJOBS_1000LOC_QUOTIENTH_MEANA_MEANMEAN_PRSEH_PCT10H_PCT25H_MEDIANH_PCT75H_PCT90A_PCT10A_PCT25A_MEDIANA_PCT75A_PCT90
010180Abilene, TXTX0All Occupationstotal66060.01.91000.0001.0020.6442930.01.89.1311.4016.4124.8236.2318990.023710.034130.051620.075370.0
110180Abilene, TXTX110000Management Occupationsmajor2910.04.544.0680.7742.8789160.02.217.3826.7436.3751.6873.8836150.055620.075640.0107500.0153670.0
210180Abilene, TXTX111021General and Operations Managersdetailed1320.07.420.0361.1940.3883990.03.512.6724.0232.9650.1172.9326350.049960.068550.0104230.0151700.0
310180Abilene, TXTX112022Sales Managersdetailed90.018.51.3170.4758.18121020.07.626.2335.9052.4168.01NaN54560.074680.0109000.0141460.0NaN
410180Abilene, TXTX112030Public Relations and Fundraising Managersdetailed40.031.50.5490.9445.9395540.020.728.7132.7836.2441.2383.8159710.068180.075380.085760.0174320.0
510180Abilene, TXTX113010Administrative Services and Facilities Managersdetailed140.012.32.0450.9237.7978600.03.524.9328.2835.0645.4555.2551850.058820.072920.094530.0114920.0
610180Abilene, TXTX113021Computer and Information Systems Managersdetailed70.030.21.0540.3259.15123040.08.526.2935.7256.2871.0497.7554690.074310.0117060.0147760.0203330.0
710180Abilene, TXTX113031Financial Managersdetailed120.08.81.8830.4057.49119580.09.625.7434.9346.1962.85NaN53540.072650.096080.0130730.0NaN
810180Abilene, TXTX113051Industrial Production Managersdetailed50.022.30.7610.5954.65113680.012.029.2036.7647.4464.4984.9260730.076460.098680.0134140.0176630.0
910180Abilene, TXTX113071Transportation, Storage, and Distribution Managersdetailed60.018.60.8900.9440.8885030.03.830.5334.4139.6347.3851.3863500.071580.082430.098550.0106880.0

Last rows

AREAAREA_TITLEPRIM_STATEOCC_CODEOCC_TITLEO_GROUPTOT_EMPEMP_PRSEJOBS_1000LOC_QUOTIENTH_MEANA_MEANMEAN_PRSEH_PCT10H_PCT25H_MEDIANH_PCT75H_PCT90A_PCT10A_PCT25A_MEDIANA_PCT75A_PCT90
14004179600Worcester, MA-CTMA536031Automotive and Watercraft Service Attendantsdetailed40.022.60.1380.1714.5030160.02.812.9713.4814.3415.2016.6426970.028050.029830.031620.034620.0
14004279600Worcester, MA-CTMA536051Transportation InspectorsdetailedNaNNaNNaNNaN25.8353720.013.713.4414.4419.7231.7241.5327950.030030.041010.065980.086370.0
14004379600Worcester, MA-CTMA537051Industrial Truck and Tractor Operatorsdetailed1280.07.94.7961.0417.9837400.02.913.7415.0917.5420.1423.8428580.031380.036490.041890.049580.0
14004479600Worcester, MA-CTMA537061Cleaners of Vehicles and Equipmentdetailed280.015.81.0560.4316.7334790.05.012.7712.9214.8719.5923.7526550.026870.030920.040750.049390.0
14004579600Worcester, MA-CTMA537062Laborers and Freight, Stock, and Material Movers, Handdetailed4770.07.817.9440.8916.5234360.01.712.8713.4314.9118.4122.9626780.027930.031000.038290.047760.0
14004679600Worcester, MA-CTMA537063Machine Feeders and Offbearersdetailed180.017.50.6601.4317.5736540.05.113.3514.3116.1120.4224.2927770.029770.033500.042470.050530.0
14004779600Worcester, MA-CTMA537064Packers and Packagers, Handdetailed1220.021.64.5921.0715.6832620.02.412.8713.4214.6816.6019.6226780.027910.030540.034530.040800.0
14004879600Worcester, MA-CTMA537065Stockers and Order Fillersdetailed4300.04.516.1611.0216.4034100.01.212.8913.6514.9918.1322.6526810.028400.031170.037700.047100.0
14004979600Worcester, MA-CTMA537081Refuse and Recyclable Material Collectorsdetailed100.016.60.3880.4522.0545870.04.913.4115.6722.9227.4530.2827900.032580.047680.057090.062980.0
14005079600Worcester, MA-CTMA537199Material Moving Workers, All OtherdetailedNaNNaNNaNNaN23.9249760.04.220.6821.6923.3725.0529.2843010.045110.048600.052100.060910.0